Hands-on with Azure Machine Learning

2020 ◽  
pp. 149-199
Author(s):  
Julian Soh ◽  
Priyanshi Singh
Keyword(s):  
Machine Learning ◽  
Hands On

Meta-analysis and machine learning-augmented mixed effects cohort analysis of improved diets among 5,847 medical trainees, providers, and patients

2021 ◽  
pp. 1-27
Author(s):  
Dominique J. Monlezun ◽  
Christopher Carr ◽  
Tianhua Niu ◽  
Francesco Nordio ◽  
Nicole DeValle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Nutrition Education ◽  
Meta Analysis ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Number Needed To Treat ◽  
Nutrition Counseling ◽  
Medical Trainees ◽  
Hands On

Abstract Objective: We sought to produce the first meta-analysis (of medical trainee competency improvement in nutrition counseling) informing the first cohort study of patient diet improvement through medical trainees and providers counseling patients on nutrition. Design: (Part A) A systematic review and meta-analysis informing (Part B) the intervention analyzed in the world’s largest prospective multi-center cohort study on hands-on cooking and nutrition education for medical trainees, providers, and patients. Settings: (A) Medical educational institutions. (B) Teaching kitchens. Participants: (A) Medical trainees. (B) Trainees, providers, and patients. Results: (A) Of the 212 citations identified (N=1,698 trainees), 11 studies met inclusion criteria. The overall effect size was 9.80 (95%CI 7.15-12.456.87-13.85; p<0.001), comparable to the machine learning (ML)-augmented results. The number needed to treat for the top performing high quality study was 12. (B) The hands-on cooking and nutrition education curriculum from the top performing study was applied for medical trainees and providers who subsequently taught patients in the same curriculum (N=5,847). The intervention compared to standard medical care and education alone significantly increased the odds of superior diets (high/medium versus low Mediterranean diet adherence) for residents/fellows most (OR 10.79, 95%CI 4.94-23.58; p<0.001) followed by students (OR 9.62, 95%CI 5.92-15.63; p<0.001), providers (OR 5.19, 95%CI 3.23-8.32, p<0.001), and patients (OR 2.48, 95%CI 1.38-4.45; p=0.002), results consistent with those from ML. Conclusions: This study suggests that medical trainees and providers can improve patients’ diets with nutrition counseling in a manner that is clinically and cost effective and may simultaneously advance societal equity.

Workshop Preview: Data Analytics and Machine Learning Hackathon 2021: A deep dive into the open-source data challenge for E&P

2021 ◽  
Vol 40 (1) ◽  
pp. 68-71
Author(s):  
Haibin Di ◽  
Anisha Kaul ◽  
Leigh Truelove ◽  
Weichang Li ◽  
Wenyi Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Learning Experience ◽  
Data Set ◽  
Deep Dive ◽  
Open Source Data ◽  
Research Workshop ◽  
Hands On ◽  
Source Data ◽  
Exploration And Production

We present a data challenge as part of the hackathon planned for the August 2021 SEG Research Workshop on Data Analytics and Machine Learning for Exploration and Production. The hackathon aims to provide hands-on machine learning experience for beginners and advanced practitioners, using a relatively well-defined problem and a carefully curated data set. The seismic data are from New Zealand's Taranaki Basin. The labels for a subset of the data have been generated by an experienced geologist. The objective of the challenge is to develop innovative machine learning solutions to identify key horizons.

Multisite Medical Student–Led Community Culinary Medicine Classes Improve Patients’ Diets: Machine Learning–Augmented Propensity Score–Adjusted Fixed Effects Cohort Analysis of 1381 Subjects

2019 ◽  
pp. 155982761989360 ◽  
Author(s):  
Zachary Stauber ◽  
Alexander C. Razavi ◽  
Leah Sarris ◽  
Timothy S. Harlan ◽  
Dominique J. Monlezun
Keyword(s):  
Machine Learning ◽  
Medical Student ◽  
Fixed Effects ◽  
Dietary Habits ◽  
Vegetable Intake ◽  
Eating Habits ◽  
Cohort Analysis ◽  
Cost Effective ◽  
Hands On ◽  
Culinary Education

Background. Healthy diet represents one of the largest single modifiable risk factors proven to decrease rates of obesity and associated chronic disease, but practical approaches to improving dietary habits through nutritional intervention are limited. Objective. To evaluate the effectiveness of a medical student–led, 6-week culinary course on participants’ dietary knowledge and behaviors, particularly focusing on the tenets of the Mediterranean diet (MedDiet). Design. This study is a prospective multisite cohort study evaluating the effects of a 6-week, hands-on community culinary education course offered at 3 sites. Participants’ knowledge of cooking skills, eating habits, and adherence to the MedDiet were evaluated using a survey prior to beginning and 6 weeks after the completion of the course. Analysis was conducted using multivariable regression to assess subjects’ diets, associated behaviors, and nutrition beliefs according to the number of classes to which they were exposed (0 to >6). Statistical results were then compared with the machine learning results to check statistical validity after selection of the top-performing algorithm from 43 supervised algorithms using 10-fold cross-validation with performance assessed according to accuracy, root relative square error, and root mean square error. Results. Among the 1381 participants, cooking classes significantly improved patients’ overall 9-point MedDiet adherence (β = 0.62, 95% CI 0.23-1.00, P = .002). Participants were more likely to meet MedDiet point requirements for fruit intake (odds ratio [OR] 2.77, 95% CI 1.46-5.23, P = .002), vegetable intake (OR 4.61, 95% CI 1.85-11.53, P = .001), legume intake (OR 2.48, 95% CI 1.45-4.26, P = .001), and olive oil use (OR 2.87, 95% CI 1.44-5.74, P = .003), and were less likely to believe that cooking takes excessive time (OR 0.31, 95% CI 0.16-0.59, P < .001). Conclusion. Hands-on culinary education courses were associated with increased MedDiet adherence and improved knowledge of healthful eating. Such interventions thus represent a cost-effective option for addressing rates of obesity and obesity-related chronic illness.

Colab Cloud Based Portable and Shareable Hands-on Labware for Machine Learning to Cybersecurity

2021 ◽  
Author(s):  
Dan Lo ◽  
Hossain Shahriar ◽  
Kai Qian ◽  
Michael Whitman ◽  
Fan Wu
Keyword(s):  
Machine Learning ◽  
Hands On

scholarly journals 311 A brief overview, comparison and practical applications of machine learning models

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 44-45
Author(s):  
Dan Tulpan
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Training Opportunity ◽  
Practical Applications ◽  
Learning Techniques ◽  
Hands On ◽  
Practical Session ◽  
Applications Of Machine Learning ◽  
Machine Learning Models

Abstract This is a hands-on workshop offered as a pre-conference training opportunity for researchers interested in applying machine learning techniques to animal science datasets with the purpose of classifying, clustering, performing linear and non-linear regressions or selecting a subset of features relevant to further studies. The objective of this workshop is to provide the audience with a way to formulate a problem such that it will be solvable by machine learning techniques and apply an exploratory analysis of various machine learning on different datasets. The workshop is structured in a hands-on format and includes a brief overview of basic notions about machine learning, a description of relevant models and evaluation metrics followed by a practical session. The practical session requires each attendee to bring their own laptop and have already installed the Waikato Environment for Knowledge Analysis (Weka) workbench for machine learning available from https://www.cs.waikato.ac.nz/ml/weka/ and all freely available machine learning models. The Weka installation of freely available machine learning models can be achieved by using the Weka Package Manager available from the Tools menu in the main application. Detailed information will be provided 2 weeks before the beginning of the workshop (week of July 5, 2020) at the following URL:http://animalbiosciences.uoguelph.ca/~dtulpan/conferences/asas2020_mlworkshop/

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